eprintid: 3675
rev_number: 8
eprint_status: archive
userid: 375
dir: disk0/00/00/36/75
datestamp: 2019-12-06 07:51:48
lastmod: 2019-12-06 07:51:48
status_changed: 2019-12-06 07:51:48
type: conference_item
metadata_visibility: no_search
creators_name: Dang, Nam Khanh
creators_name: Abdallah, Abderazek Ben
creators_id: dnk0904@gmail.com
creators_id: benab@u-aizu.ac.jp
title: An Efficient Software-Hardware Design Framework for Spiking Neural Network Systems
ispublished: inpress
subjects: ECE
divisions: lab_sis
abstract: Spiking Neural Network (SNN) is the third generation
of Neural Network (NN) mimicking the natural behavior
of the brain. By processing based on binary input/output, SNNs offer lower complexity, higher density and lower power consumption.
This work presents an efficient software-hardware design
framework for developing SNN systems in hardware. In addition, a design of low-cost neurosynaptic core is presented based on packet-switching communication approach. The evaluation results show that the ANN to SNN conversion method with the size 784:1200:1200:10 performs 99% accuracy for MNIST while the unsupervised STDP archives 89% with the size 784:400 with recurrent connections. The design of 256-neurons and 65k synapses is also implemented in ASIC 45nm technology with an area cost of 0.205 mm2.
date: 2019
date_type: published
full_text_status: restricted
pres_type: paper
event_title: The International Conference on Internet of Things, Embedded Systems and Communications (IINTEC 2019
event_type: conference
refereed: TRUE
citation:   Dang, Nam Khanh and Abdallah, Abderazek Ben  (2019) An Efficient Software-Hardware Design Framework for Spiking Neural Network Systems.  In: The International Conference on Internet of Things, Embedded Systems and Communications (IINTEC 2019.    (In Press)  
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3675/1/IINTEC19_Tun_CRP.pdf